Incorporating intra-class variance to fine-grained visual recognition

• Published in: 2017 IEEE International Conference on Multimedia and Expo (ICME) pp. 1452 - 1457
• Main Authors: Yan Em, Feng Gag, Yihang Lou, Shiqi Wang, Tiejun Huang, Ling-Yu Duan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2017
• Subjects: Automobiles; Character recognition; Feature extraction; Fine-grained visual recognition; Image color analysis; Intra-class variance; Measurement; Metric learning; Training; Visualization
• ISSN: 1945-788X
• DOI: 10.1109/ICME.2017.8019371

Fine-grained visual recognition aims to capture discriminative characteristics amongst visually similar categories. The state-of-the-art research work has significantly improved the fine-grained recognition performance by deep metric learning using triplet network. However, the impact of intra-category variance on the performance of recognition and robust feature representation has not been well studied. In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition. Through partitioning training images within each category into a few groups, we form the triplet samples across different categories as well as different groups, which is called Group Sensitive TRiplet Sampling (GS-TRS). Accordingly, the triplet loss function is strengthened by incorporating intra-class variance with GS-TRS, which may contribute to the optimization objective of triplet network. Extensive experiments over benchmark datasets CompCar and VehicleID show that the proposed GS-TRS has significantly outperformed state-of-the-art approaches in both classification and retrieval tasks.